Biomarkers, Prognosis, and Prediction Factors





Breast cancer is a heterogeneous group of neoplasms originating from the epithelial cells lining the milk ducts. This heterogeneity has been observed in histology for a long time and formed the backbone of the traditional pathology-driven classification of breast cancer. Multiple studies have shown that this heterogeneity in the histopathologic features of breast cancer was associated with clinical outcomes. More recently, the pathology-driven classification has been replaced by molecular classifications based on hormone receptors, human epidermal growth factor receptor 2 (HER2), and Ki-67 status. These molecular classifications reflect genetic tumor heterogeneity and have strong associations with the prognosis of various breast cancers. As a result, there is hope that the development of targeted therapies to these molecular subtypes will lead to improved outcomes of breast cancer. Additionally, a lot of research has been performed to identify imaging features on breast imaging examinations, such as breast magnetic resonance imaging (MRI), that may serve as imaging biomarkers for these molecular subtypes of breast cancer. This chapter reviews the current understanding of the intrinsic molecular subtypes of breast cancer, with emphasis on the imaging features of these molecular subtypes on breast MRI and diffusion-weighted imaging (DWI).


Background


In 2022 an estimated 287,850 new cases of invasive breast cancer are expected to be diagnosed in women in the United States, along with 51,400 new cases of noninvasive (in situ) breast cancer. An estimated 43,250 women are expected to die in 2022 from breast cancer, making it the second-leading cause of cancer death among women in the United States. 1 Death rates have been steady in women under 50 since 2007 but have continued to drop in women over 50. The overall death rate from breast cancer decreased by 1.3% per year from 2013 to 2017. These decreases are thought to be the result of treatment advances and earlier detection through screening. Although screening mammography accounts for most of this early detection, studies have found that screening with breast MRI leads to earlier detection of biologically relevant cancers among high-risk and average-risk women, which are often occult on mammography.


Breast MRI is an indispensable modality, along with mammography and ultrasound (US). Its clinical indications are staging of known cancer, screening for breast cancer in women at increased risk, and evaluation of response to neoadjuvant chemotherapy. Unlike conventional mammography and US, MRI is a functional technique. Contrast material-enhanced MRI evaluates the permeability of blood vessels by using an intravenous contrast agent (gadolinium chelate) that shortens the local T1 time, leading to a higher signal on T1-weighted images. The underlying principle is that neoangiogenesis leads to formation of leaky vessels that allow for faster extravasation of contrast agents, thus leading to rapid local enhancement and the detection of breast cancer.


In the specific setting of MRI for high-risk screening in BRCA1/2 mutation carriers, the reported sensitivities are between 75.2% and 100% and specificities between 83% and 98.4%. The cancer detection rate among known BRCA1/2 carriers was 26.2 per 1000, compared with 5.4 per 1000 in high-risk nonmutation carriers. Compared with the earlier trials, the sensitivities and specificities of breast MRI continue to improve due to two major factors. First, MRI scanners, coils, and scan protocols have evolved, leading to marked improvement in image quality and spatial resolution. Second, radiologists have gained significantly more experience in the interpretation of breast MRI examinations. Despite this progress, false-positive findings remain a limitation of breast MRI examinations.


False-positive MRI lesions have indeterminate morphological and kinetic features that turn out to be benign at biopsy but cannot be classified as certainly benign based upon the imaging examination alone. Because benign breast lesions are frequent, a multiparametric breast MRI examination that includes DWI assists with lesion characterization. DWI can help visualize and quantify random movement of water molecules in tissue, influenced by tissue microstructure and cell density. This is achieved by applying motion-sensitizing gradients (described by the b value) to an essentially T2-weighted echo-planar imaging sequence. Breast cancers show decreased water diffusion, primarily attributed to increased cell density, leading to higher signal intensity on DWI. The apparent diffusion coefficient (ADC) is a quantitative measure of diffusivity derived from DWI. Values are usually expressed in 10 −3 mm 2 /s. Because of the hindered diffusion in cancers, mean ADCs are generally low (range 0.8–1.3 × 10 −3 mm 2 /s) compared with those in benign lesions (range 1.2–2.0 × 10 −3 mm 2 /s). Consequently, cancers have a low signal intensity on the derived ADC maps, and this feature allows DWI to improve the differentiation between benign and malignant breast lesions. Additional roles for DWI include lesion detection on a screening MRI, using DWI as a prognostic indicator, and predicting response to treatment. This chapter will explain how quantitative DWI findings provide information on prognosis and prediction factors of breast cancers and help lesion management. Please see related chapters on breast lesion characterization (see Chapter 3 ), monitoring response to treatment (see Chapter 5 ), and multiparametric imaging, radiomics, and artificial intelligence (see Chapter 10 ).


Classification of Breast Cancer


Breast cancer is a heterogeneous disease comprising several molecular and genetic subtypes, each with characteristic biological behavior and imaging patterns. Traditional classification of breast cancer is based on the clinicopathologic analysis of tumors, with classes of breast cancer defined by histopathologic features, including the pattern of architectural growth (e.g., cribriform, papillary) and the nuclear grade (low, intermediate, or high). Treatment choices are partially determined by the tumor size, local invasion, and lymph node involvement or distant metastases, as defined by the American Joint Committee on Cancer’s (AJCC) TNM staging classification (7th edition). However, this traditional classification of breast cancer, based on the histopathologic features, offers limited prognostic value. Although survival rates correlate best with tumor size and the presence of axillary metastasis, breast cancer patients at the same anatomical stage of disease can have markedly different clinical courses and clinical outcomes. Fortunately, developments in the field of molecular biology have allowed breast cancers to be analyzed by their expression of specific biomarkers. As a result, the eighth edition of the AJCC’s TNM staging classification incorporates this genetic information into the traditional classification scheme.


Genomic Expression Pattern Analysis and Molecular Subtype of Breast Cancer


Technical developments in DNA microarrays, specifically genomic expression pattern analysis using hierarchical clustering, are the basis for the molecular classification of major subtypes of breast cancer. In 2000 Perou and colleagues tested 65 breast tumors against DNA microarrays representing 8102 genes. These investigators noted that the molecular portraits of the tumors, derived from patterns of gene expression, disclosed some groupings. Tumors were clustered in terms of growth rate, activation of specific signaling pathways, and cellular composition. For example, the more proliferative tumors overexpressed genes such as Ki-67, a marker of cellular proliferation; this expression correlated with increased mitotic indexes at histopathologic examination. Based on the persistent differences in their gene expression patterns, the authors divided breast cancers into two large categories based on their pattern of gene expression. When tumor cells manifested characteristics similar to the epithelial cells lining the milk ducts, expressing, for example, cytokeratin 8/18 and genes associated with the estrogen receptor (ER), the cancers were labeled luminal cancers. Alternatively, when cancer cells displayed characteristics similar to myoepithelial cells (also known as basal cells) that line the inner surface of the basement membranes, expressing, for example, cytokeratin 5/6 and laminin, the cancers were grouped into the basal category.


As a result, two large categories of luminal breast cancers and basal breast cancers were defined and dichotomized on the basis of the presence or absence of ERs. ER biology was identified as a key player in breast carcinogenesis, defining the morphology and the clinical behavior of the final tumor, whereas other parameters, such as tumor grade, were found to be less important. Luminal tumors also were generally characterized by an absence of overexpression of the HER2 gene, a proto-oncogene that stimulates cellular growth. Based on genomic profiling at the DNA, microRNA, and protein levels, researchers in The Cancer Genome Atlas (TCGA) Network refined these classifications into four intrinsic molecular subtypes of breast cancer: luminal A, luminal B, HER2-enriched (HER2+), and triple-negative (TN) and/or basal-like tumors ( Table 4.1 ). Since then, multiple studies confirmed that each subtype of breast cancer has a unique response to therapy, disease-free survival (DFS), and overall survival (OS). As a result, subtype-based recommendations for systemic therapies have been implemented in clinical practice.



Table 4.1

Clinical and Immunohistochemical Surrogates for Molecular Subtypes of Breast Cancer






































































Intrinsic Subtype Subtype ER PR HER2 Ki-67 Histological Grade Recurrence Risk Score Frequency (%) Comments
Luminal A Luminal A-like Positive Positive>20% Negative Low Generally Grade 1 or 2 Low 50–55 Best prognosis
Luminal B Luminal B-like (HER2−) Positive Negative or low <20% Negative High Generally Grade 3 High 20 (both types of luminal B) Less favorable than luminal A
Luminal B-like (HER2+) Positive Any Overexpressed or amplified Any Generally Grade 3 High
ErbB2 overexpression HER2+ (nonluminal) Negative Negative Overexpressed or amplified Any Generally Grade 3 N/A 15 Improved with HER2-targeted therapy
Triple negative Triple negative Negative Negative Negative Any Generally Grade 3 N/A 10–20 May improve with novel agents a

ER, estrogen receptor; HER2 , human epidermal growth factor receptor 2; PR , progesterone receptor.

a Immunotherapy, antibody-drug conjugates (ADCs), and poly (adenosine diphosphate-Q22 a ribose) polymerase inhibitors.



Distribution and Prognosis of Molecular Subtypes of Breast Cancer


These subtypes are unevenly distributed in breast cancer patients and associated with different tumor phenotypes and distinct variations in response to therapy and survival. Patients with luminal A tumors have the most favorable prognosis, followed by those with luminal B tumors, who have an intermediate prognosis. On the other end of the spectrum, TN and HER2+ subtypes are associated with an unfavorable prognosis, but with the introduction of targeted drugs, such as trastuzumab or pertuzumab, the natural course of disease of the HER2+ subtype is nowadays more favorable, whereas the triple-negative subtype is associated with an unfavorable prognosis.


In the clinical and research settings, molecular subtypes are derived by invasive sampling. Several molecular assays are commercially available, including Oncotype DX (Genomic Health, Redwood City, CA); MammaPrint (Agendia, Irvine, CA); Mammostrat (Clarient Diagnostic Services, Aliso Viejo, CA); PAM50 (Prosigna; NanoString, Seattle, WA); EndoPredict (Sividon/Myriad Genetics, Salt Lake City, UT); and MapQuant Dx Genomic Grade index (Ipsogen/QIAGEN; Venlo, the Netherlands). Oncotype DX and MammaPrint, both of which are approved by the Food and Drug Administration, have shown predictive and prognostic abilities for evaluating the risk of developing distant metastasis and predicting the benefit of adjuvant chemotherapy. However, biopsies of small tumor regions may not be representative of the genetic, epigenetic, and/or phenotypic alterations of the entire tumor. A common alternative is to use immunohistochemical (IHC) surrogates for the definition of molecular subtypes (see Table 4.1 ). There is variable agreement between classifications via these surrogates and formal genetic testing (41%–100%). Given these limitations and the relatively high cost of assays, there is a strong demand for more accurate, noninvasive means of differentiating molecular subtypes, which presents a unique opportunity for advanced medical imaging, specifically with breast MRI and DWI.


Luminal Tumors


Luminal A tumors are the most common type of breast cancer, accounting for 50% to 55% of all tumors. They are characterized by high genetic expression of the ER and progesterone receptors (PRs), as well as many other genes expressed by the epithelial cells that line the lumen of the terminal duct lobular unit, where most breast cancers arise. These cancers are usually low-grade tumors, without amplification of the HER2/neu proto-oncogene and with a low Ki-67 proliferative index. Overall, luminal A breast cancer is associated with the most favorable prognosis, with a 5-year OS and relapse-free survival rate of more than 80% in 2001. This excellent prognosis is in part because expression of steroid hormone receptors is predictive of a favorable response to hormonal therapy. Luminal A tumors progress slowly over time, and the chance of DFS survival is higher than with other subtypes.


Luminal B tumors account for 20% of all tumors. These cancers also express ERs and PRs but have greater proliferative activity, as can be assessed through Ki-67 levels; these cancers are usually mid- to high-grade tumors. Luminal B breast cancers characteristically do not overexpress HER2/neu, but approximately 30% of them will be HER2-enriched. The prognosis of patients with luminal B breast cancer is often poorer than that for patients with luminal A tumors. Five-year OS and relapse-free survival rates were approximately 40% in 2001. Although ER status and PR status are predictors of response to endocrine therapy, the clinical outcome cannot reliably be predicted solely from the ER and PR status, and analysis of other cellular markers and tumor characteristics is required for optimal assessment of outcome and to determine the need for chemotherapy.


Mammography, Ultrasound, and Magnetic Resonance Imaging Features of Luminal Tumors


Luminal A tumors are most commonly screening-detected masses with spiculated margins and associated architectural distortion. Luminal B masses exhibit indistinct, microlobulated, or spiculated margins but are less likely to demonstrate distortion. Ko and colleagues reported on the appearance of 93 ER-positive (ER+) HER2-negative (HER2-) breast cancers at mammography. These cancers appeared most often as irregular masses (45%) or irregular masses with calcifications (28%). Overall, microcalcifications were seen in 41% of these cancers. The typical sonographic appearance of luminal tumors is an irregular mass with angular or microlobulated margins, with posterior acoustic shadowing.


On MRI, luminal A and luminal B tumors often present as an enhancing irregular mass with spiculated margins and uncommonly as nonmass enhancement. Uematsu and colleagues described the appearance of 117 ER+ HER2- breast cancers at MRI. Forty-six percent of these lesions were unifocal, 44% were multifocal, and 9% were multicentric. These investigators reported mass enhancement in the majority of these tumors (67%), with the remainder being areas of nonmass enhancement (33%), most often segmental. The masses were most often irregular (32%) or oval (38%) in shape, with irregular margins in 86%. Heterogeneous internal enhancement was seen in 97% of the tumors, with plateau or washout kinetics in 79%. Eighty-five percent of the ER+ HER2-tumors were iso- to hypointense on T2-weighted MR images.


Diffusion-Weighted Imaging of Luminal Tumors


DWI is a useful tool to differentiate malignant lesions from benign lesions. It also provides information on tumor biology and microstructural features. As a result, studies were conducted to correlate ADC values and breast cancer prognostic factors. We will review the literature on the association of DWI with luminal tumors. Examples of luminal A and luminal B breast cancers evaluated on DWI are illustrated in Figs. 4.1 and 4.2 , respectively.




Fig. 4.1


Fifty-five-year-old woman presenting with a newly diagnosed invasive ductal carcinoma, moderately differentiated, luminal A tumor (ER+/PR+, HER2−, Ki-67 10%).

(A) Sagittal T1-weighted postcontrast subtraction images and (B) axial T1=weighted postcontrast images shows a 2.2-cm spiculated heterogeneously enhancing mass (arrow) in the medial left breast. (C) Diffusion-weighted b 800 image shows the cancer is hyperintense (arrow) . (D) The apparent diffusion coefficient (ADC) map (red circle) generated with b 0 and b 800 values shows low values in the cancer, consistent with restricted diffusion.



Fig. 4.2


Forty-eight-year-old woman presenting with a palpable mass in the left breast.

(A) Sagittal T1-weighted postcontrast subtraction images and (B) axial T1-weighted postcontrast images showed a 3-cm spiculated enhancing mass (white arrow) in the left breast. (C) In addition, there is an enlarged lymph node (arrow) in the left axilla. (D) Diffusion-weighted b 800 image shows the cancer is hyperintense (arrow) . (E) The apparent diffusion coefficient (ADC) map (red circle) (generated with b 0 and b 800 values) is heterogeneous showing areas with and without restricted diffusion. Subsequent biopsies showed invasive lobular carcinoma, well-differentiated luminal B tumor (ER+/PR+ HER2−, Ki-67 18%) in the left breast and a metastatic lymph node in the left axilla. The patient was treated with lumpectomy followed by chemotherapy and radiation. Nine years later, the patient underwent a positron-emission tomography/computed tomography (PET/CT) for persistent lower back pain after a motor vehicle accident. (E–G) PET/CT showed multiple new hypermetabolic paraesophageal, retrocrural, and retroperitoneum lymph nodes (arrows) . Biopsy of a retroperitoneal lymph node confirmed metastatic breast cancer.


The ability of DWI to serve as an imaging biomarker that is associated with particular molecular subtype and to prognosticate the response to treatment is an area of robust research. Most of these DWI studies are summarized in Table 4.2 . Some investigators found that the percent of ER or PR expression accounted for differences in ADC values.



Table 4.2

Summary of Diffusion-Weighted Imaging Parameters According to Prognostic Factors




































































































































































































































































































Reference Patients ( n ) Field Strength (T) b -values Significant Parameters Significant Outcomes Nonsignificant Outcomes
Kim et al. 2009 67 1.5 0, 1000 Median ADC ER (marginal significance) PR, HER2, p53, Ki-67, EGFR
Jeh et al. 2011 107 1.5, 3 0, 1500 for 1.5 T;0, 750 for 3 T Mean ADC ER, HER2 PR, Ki-67, EGFR
Choi et al. 2012 355 1.5 0, 1000 Mean ADC ER, PR, Ki-67 HER2, LN
Martincich et al. 2012 190 1.5 0, 900 Median ADC ER, HER2, HER2+ subtype vs. luminal
Kamitani et al. 2013 81 1.5 0, 500, 1000 Mean ADC ER, PR, LN HER2, nuclear grade, vascular invasion
Cipolla et al. 2014 92 3 0, 1000 ADC Histological grade
Kim et al. 2015 173 3 0, 750 ADC histogram parameters HER2, Ki-67, LN, subtypes HG, ER, PR
Park et al. 2015 110 3 0, 1000 Mean ADC HER2 HG, LN, ER, PR
Belli et al. 2015 289 1.5 0, 1000 Mean ADC HG, LN Tumor size
Molinari et al. 2015 115 1.5 0, 1000 ADC Ki-67, HG, luminal B vs. other subtypes
Arponen et al. 2015 112 3 0, 200, 400, 600, 800 ADC LN, HG, PR, NPI ER, HER2, Ki-67
Cho et al. 2016 50 3 0, 30, 70, 100, 150, 200, 300, 400, 500, 800 ADC, IVIM parameters (Dt, Dp, fp) ER, PR, HER2, Ki-67, subtypes
Kim et al. 2016 275 3 0, 30, 70, 100, 150, 200, 300, 400, 500, 800 ADC, IVIM parameters (Dt, fp) Ki-67, luminal B vs. other subtypes
Durando et al. 2016 212 3 0, 1000 ADC LVI Tumor size, HG, ER, PR, HER2, LN
Kitajima et al. 2016 214 3 0, 1000 Mean ADC Ki-67, tumor size, LN, TNM stage, IDC vs. ILC ER, PR, HER2, subtype
Shin et al. 2016 138 3 0, 1000 ADC Tumor cellularity, Ki-67, PLI TILs, LN, tumor size
Lee et al. 2017 72 3 0, 25, 50, 75, 100, 150, 200, 300, 500, 800 ADC, IVIM parameters (Ds) ER, HG, subtype, Ki-67
Kawashima et al. 2017 134 3 0, 20, 40, 80, 120, 200, 400, 600, 800 ADC, IVIM parameters (D) Luminal A vs. luminal B, Ki-67
Suo et al. 2017 101 3 0, 10, 30, 50, 100, 150, 200, 500, 800, 1000, 1500, 2000, 2500 ADC, α, Df, Ds, f, DDC, MD Ki-67, LN, ER
Fan et al. 2017 82 3 50, 1000 ADC (tumor, peritumoral) Ki-67
Vidic et al. 2018 51 3 0, 10, 20, 30, 40, 50, 70, 90, 120, 150, 200, 400, 700 Combined diffusion model HER2 status of ER+ tumors
Iima et al. 2018 199 3 5, 10, 20, 30, 50, 70, 100, 200, 400, 600, 800, 1000, 1500, 2000, 2500 sADC 200–1500 PR, subtypes
Fan et al. 2018 126 3 50, 1000 Mean ADC Subtypes
Liu et al. 2018 151 3 0, 1000 ADC ER, PR, HER2
Amornsiripanitch et al. 2018 107 3 0, 800 ADC mean, CNR HG, Ki-67, Oncotype DX RS
Shen et al. 2018 71 3 0, 600 ADC Ki-67, subtypes
Zhuang et al. 2018 80 3 0, 800 Min ADC, max ADC, ΔADC Ki-67
Surov et al. 2018 870 1.5, 3 0, 1000; 0, 900 for 1.5 T; 50, 1000; 0, 0, 800; 0, 1000 for 3 T Mean ADC Ki-67 Nuclear grade
Thakur et al. 2018 31 3 0, 600, 1000 Mean ADC Oncotype DX RS
Igarashi et al. 2018 140 1.5 0, 1000, 1500 ADC (tumor, peritumoral), ratio LVI
Suo et al. 2019 134 3 0, 800, 1500 Mean ADC, entropy of ADC ER, PR, HER2, Ki-67, luminal vs. HER2+ subtypes
Horvat et al. 2019 107 3 0, 850 Mean ADC, max ADC ER, PR, luminal vs. nonluminal subtypes
Kim et al. 2019 258 3 0, 1000 ΔADC Distant metastasis-free survival
Fogante et al. 2019 125 1.5 0, 800 Mean ADC TILs, IDC vs. ILC
Tang et al. 2020 114 1.5 50, 800 ADC histogram parameters TILs, Ki-67

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Oct 30, 2022 | Posted by in MAGNETIC RESONANCE IMAGING | Comments Off on Biomarkers, Prognosis, and Prediction Factors
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